Mapping the brain to build better machines. The Intelligence Advanced Research Projects Activity (IARPA) has funded the Machine Intelligence from Cortical Networks program (MICrONS) to the tune of $100m over 5 years. Three teams will monitor neuronal activity from tens of thousands of neurons in a target cube of the vision cortex to create a 3D model of neuronal circuitry. This model will be used to discover rules governing the circuit that could help us understand feedback loops between neurons in order to build more biologically inspired artificial neural networks.

Toyota announces their new Ann Arbor, MI-based AI and robotics research site following those in Palo Alto, CA and Cambridge, MA. Of note, Toyota already has two Technical Centers conducting autonomous vehicle research in the area, which is also home to a 23-acre mini-city testing ground for pilot vehicles. Remember, Toyota is also a likely bidder for the Google-owned Boston Robotics, the maker of Big Dog and humanoid robots.

Deep3D: Fully Automatic 2D-to-3D VideoConversion with Deep Convolutional Neural Networks, University of Washington, code here. 3D movies are growing in popularity (remember Avatar in 2008?), but they’re expensive to produce using either 3D cameras or 2D video manually converted to 3D. To automatically convert 2D to 3D, one needs to infer a depth map for each pixel in an image (i.e. how far each pixel is from the camera) such that an image for the opposing eye can be produced. Existing automated neural network-based pipelines require image-depth pairs for training, which are hard to procure. Here, the authors use stereo-frame pairs that exist in already-produced 3D movies to train a deep convolutional neural network to predict the novel view (right eye’s view) from the given view (left eye’s view) using an internally estimated soft (probabilistic) disparity map.

“Why Should I Trust You?” Explaining the Predictions of Any Classifier, University of Washington. Code here. A key hurdle to the mass adoption of machine learning models in fault intolerant commercial settings (e.g. finance, healthcare, security) is the ability to provide explanations as to why certain predictions were made. Many models, especially neural networks, are today functionally black boxes with trust in their performance relying on cross validation accuracy. The authors present a model-agnostic algorithm that presents textual or visual artifacts using interpretable representations of underlying data (not necessarily a model’s features) to provide the user with a qualitative understanding of what a given model is basing its classification predictions on. This is very nifty work. Further explanation here.

Dynamic Memory Networks for Visual and Textual Question Answering, MetaMind. A year ago, the MetaMind team published the dynamic memory network, a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. In this work, the team introduce a new input module to handle images instead of text, such that the network can now answer natural language questions from its understanding of features in the image. Specifically, the input module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text.

The Curious Robot: Learning Visual Representations via Physical Interactions, Carnegie Mellon University. The task of learning visual representations in the real world with CNNs typically requires a large dataset of labeled image examples. This group instead explores whether a Baxter robotic arm can learn visual representations only by performing four physical interactions: push, poke, grasp and active vision. They show that by experiencing 130k of these interactions with household objects (e.g. cups, bowls, bottles) and using each data point for back-propagation through a CNN, the network can learn some generalised features that helps it classify household object images on ImageNet without having seen any labeled images before.

Deep learning for chatbots, part 1 - Introduction. Given the excitement around chat interfaces and their ability to evolve user experiences for today’s generation of technophiles, here’s a piece that describes where we’re at technically, what’s possible and what will stay nearly impossible for at least a little while. This series will follow up with implementation details in upcoming posts.

Twiggle, the fairly quiet Tel Aviv-based startup working on an improved core technology stack focused on e-commerce search, announced a $12.5m Series A led by Naspers, the publicly traded South African internet and media group.

Kreditech, the German online lender underwriting loans using non-traditional data points, closed out the final $11m of its $103m Series C with an investment from the International Finance Corporation (a division of The World Bank). *Jose﻿ Garcia Moreno-Torres﻿﻿, Kreditech’s Chief Data Science Officer, is presenting at our second Playfair AI event on July 1st in London.

Salesforce acquired MetaMind, founded by Stanford PhD Richard Socher who was working on NLP and later vision, for an undisclosed sum (purportedly an acquihire). The business raised $8m from Khosla Ventures and Salesforce Founder/CEO Mark Benioff. Of note, Richard writes “[Salesforce will use MetaMind to] automate and personalize customer support, marketing automation, and [improve] many other business processes. We’ll extend Salesforce’s data science capabilities by embedding deep learning within the Salesforce platform.” Very exciting indeed.

Anything else catch your eye? Just hit reply! I’m actively looking for entrepreneurs building companies that build/use AI to rethink the way we live and work.